Postdoctoral Fellowships for Sustainability Solutions at USC

 

The Postdoctoral Fellowships for Sustainability Solutions program aims to accelerate sustainability research; train future leaders in academia, government and non-governmental organizations, and industry; and support discovery, evaluation, and implementation of innovative solutions to sustainability problems.  

This postdoctoral fellowship seeks to advance the capacities of early-career scholars and researchers to conduct interdisciplinary research on sustainability problems. An interdisciplinary and diverse cohort of fellows will address challenges in one or more of the following areas: 

• human health and well-being; 
• infrastructure and the built environment;
• natural environment and ecosystem services; 
• communications, policy, and institutions; 
• risk analysis and economic impacts.  

How to apply

Candidates must have earned a doctoral degree (e.g., Ph.D., Sc.D., M.D., J.D.) within the previous five yearsin a relevant field of study prior to the start of the appointment.  

 

Applications will be submitted online. Candidates should submit the following materials to the on-line application:

 

•      Cover letter (1 page)

•      Curriculum Vitae

•      Research proposal (up to 2 pages)

•      Names, contact information, and a joint statement of endorsement from two (2) USC faculty mentors with primary appointments in two different USC schools. The letter should provide a brief statement regarding their plan to provide career support and mentorship for the duration of the appointment. Examples of mentorship activities include, but are not limited to:

§  Conduct yearly reviews of the postdoc's IDP

§  Attend planned events for the program

§  Assist in preparing the research design and executing the research activities.

§  Arrange forums for the presentation, dissemination, and/or critique of the applicant’s research.

§  Identify potential publication sources and assisting in the preparation and submission of articles and manuscripts.

§  Connect the postdoc to other relevant investigators at USC and at other institutions.

§  Identify external funding sources and assisting in the preparation of grant proposals.

•      Statement that highlights contributions to diversity, equity, and inclusion (up to 1 page)

•      Names and e-mail addresses of two (2) references for letters of recommendation 

 

Applications will be evaluated starting at the beginning of November 2022. For full consideration of your application, please submit your material no later than 11/15/2022.

 

To find more information on the application process and to apply, click here

Data Analytics Workshop for Advanced Manufacturing was organized at USC on August 14, 2022

The invited speakers include

• Prof. Hao Yan, Arizona State University
• Prof. Andi Wang, Arizona State University
• Prof. Cesar Ruiz,  University of Oklahoma
• Prof. Yuanxiang Wang, Tongji University 

PhD students from USC Huang Lab, Weizhi Lin, Chris Henson, and Gabriel Gu presented their dissertation research on machine learning for Additive Manufacturing. Undergraduate researchers Yilin and Chengxi shared their summer research work. 

The workshop ended with a great dinner at Savoca at LA Live!

An Impulse Response Formulation for Small-Sample Learning and Control of Additive Manufacturing Quality

This work establishes an impulse response formulation of layer-wise AM processes to relate design inputs with the deformed final products. To enable prescriptive learning from a small sample of printed parts with different 3D shapes, we develop a fabrication-aware input-output representation, where each product is constructed by a large amount of basic shape primitives. The impulse response model depicts how the 2D shape primitives (circular sectors, line segments, and corner segments) in each layer are stacked up to become final 3D shape primitives. A geometric quality of a new design can therefore be predicted through the construction of learned shape primitives. Essentially, the small-sample learning of printed products is transformed into a large-sample learning of printed shape primitives under the impulse response formulation of AM. This fabrication-aware formulation builds the foundation for applying well-established control theory to the intelligent quality control in AM. It not only provides theoretical underpinning and justification of our previous work, but also enable new opportunities in ML4AM. As an example, it leads to transfer function characterization of AM processes to uncover process insights. It also provides block-diagram representation of AM processes to design and optimize the control of AM quality.

https://doi.org/10.1080/24725854.2022.2113186

Share two new journal papers: one extending the fabrication-aware convolution learning framework to a broader class of 3D geometries for 3D printing accuracy control, and the other providing accuracy control for Wire and Arc Additive Manufacturing.

Share two new journal papers: one extending the fabrication-aware convolution learning framework to a broader class of 3D geometries for 3D printing accuracy control, and the other providing accuracy control for Wire and Arc Additive Manufacturing:

• Yuanxiang Wang*, Cesar Ruiz*, and Q. Huang, 2022, “Learning and Predicting Shape Deviations of Smooth and Non-Smooth 3D Geometries through Mathematical Decomposition of Additive Manufacturing, ” IEEE Transactions on Automation Science and Engineering, DOI: 10.1109/TASE.2022.3174228, in press.

• Cesar Ruiz*, Davoud Jafari, Vignesh V. Subramanian, Tom H.J. Vaneker, Wei Ya, and Qiang Huang, 2022, “Prediction and Control of Product Shape Quality in Wire and Arc Additive Manufacturing Using Generalized Additive Models,” ASME Transactions, Journal of Manufacturing Science and Engineering, DOI: 10.1115/1.4054721, in press.